Evapotranspiration by woody phreatophytes in the Humboldt River valley near Winnemucca, Nevada, with a section on soil-moisture determinations

1970 ◽  
Author(s):  
T.W. Robinson ◽  
A.O. Waananen
Keyword(s):  
Soil Moisture ◽  
River Valley

scholarly journals Impact of Spring Soil Moisture on Surface Energy Balance and Summer Monsoon Circulation over East Asia and Precipitation in East China

2011 ◽  
Vol 24 (13) ◽  
pp. 3309-3322 ◽  
Author(s):  
Renhe Zhang ◽  
Zhiyan Zuo
Keyword(s):  
Soil Moisture ◽  
Yangtze River ◽  
Surface Energy Balance ◽  
Summer Precipitation ◽  
Western Pacific Subtropical High ◽  
Yangtze River Valley ◽  
River Valley ◽  
East China ◽  
Monsoon Circulation ◽  
The Yangtze River

Abstract Numerous studies have been conducted on the impact of soil moisture on the climate, but few studies have attempted to diagnose the linkage between soil moisture and climate variability using observational data. Here, using both observed and reanalysis data, the spring (April–May) soil moisture is found to have a significant impact on the summer (June–August) monsoon circulation over East Asia and precipitation in east China by changing surface thermal conditions. In particular, the spring soil moisture over a vast region from the lower and middle reaches of the Yangtze River valley to north China (the YRNC region) is significantly correlated to the summer precipitation in east China. When the YRNC region has a wetter soil in spring, northeast China and the lower and middle reaches of the Yangtze River valley would have abnormally higher precipitation in summer, while the region south of the Yangtze River valley would have abnormally lower precipitation. An analysis of the physical processes linking the spring soil moisture to the summer precipitation indicates that the soil moisture anomaly across the YRNC region has a major impact on the surface energy balance. Abnormally wet soil would increase surface evaporation and hence decrease surface air temperature (Ta). The reduced Ta in late spring would narrow the land–sea temperature difference, resulting in the weakened East Asian monsoon in an abnormally strengthened western Pacific subtropical high that is also located farther south than its normal position. This would then enhance precipitation in the Yangtze River valley. Conversely, the abnormally weakened East Asian summer monsoon allows the western Pacific subtropical high to wander to south of the Yangtze River Valley, resulting in an abnormally reduced precipitation in the southern part of the country in east China.

A new methodology to monitor soil moisture over a complex arctic environment, Kobuk River valley, Alaska

2013 ◽  
Vol 4 (3) ◽  
pp. 251-260 ◽  
Author(s):  
Marius Necsoiu ◽  
Nicolas Longépé ◽  
Donald M. Hooper
Keyword(s):  
Soil Moisture ◽  
River Valley ◽  
Arctic Environment

scholarly journals Effect of Spring Precipitation on Summer Precipitation in Eastern China: Role of Soil Moisture

2017 ◽  
Vol 30 (22) ◽  
pp. 9183-9194 ◽  
Author(s):  
Li Liu ◽  
Renhe Zhang ◽  
Zhiyan Zuo
Keyword(s):  
Soil Moisture ◽  
Yangtze River ◽  
Asian Summer Monsoon ◽  
Eastern China ◽  
Summer Precipitation ◽  
Northeastern China ◽  
Yangtze River Valley ◽  
River Valley ◽  
The Yangtze River ◽  
Spring Precipitation

The relation of spring (March–May) to summer (July–August) precipitation in eastern China is examined using observed data. It is found that when spring precipitation from the lower and middle reaches of the Yangtze River valley to northern China (the YRNC region) is higher (lower), more (less) summer precipitation occurs in northeastern China and the lower and middle reaches of the Yangtze River valley, and less (more) in southeastern China. The analysis of physical mechanism showed that higher (lower) spring precipitation in the YRNC region is closely related to wet (dry) spring soil moisture, which decreases (increases) the surface temperature and sensible heat flux in late spring. Because the memory of spring soil moisture in the YRNC region reaches about 2.4 months, the surface thermal anomaly lasts into the subsequent summer, resulting in a weak (strong) East Asian summer monsoon. A weak East Asian summer monsoon corresponds to an anomalous anticyclone and a cyclone over southeastern and northeastern China, respectively, in the lower troposphere. The anomalous anticyclone depresses the summer precipitation in southeastern China, and the anomalous cyclone promotes precipitation over northeastern China. The abnormal northerly and southerly winds associated with the anomalous cyclone and anticyclone, respectively, converge in the lower and middle reaches of the Yangtze River valley, inducing more summer precipitation there.

Effect of preceding soil moisture-snow cover anomalies around Turan Plain on June precipitation over the southern Yangtze River valley

2021 ◽  
Vol 264 ◽  
pp. 105853
Author(s):  
Kejun He ◽  
Ge Liu ◽  
Renguang Wu ◽  
Sulan Nan ◽  
Jingxin Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Snow Cover ◽  
Yangtze River ◽  
Yangtze River Valley ◽  
River Valley

scholarly journals On the ability of Sentinel-1 backscatter to detect soil moisture and vegetation changes caused by irrigation fluxes over the Po River Valley (Italy)

2021 ◽  
Author(s):  
Sara Modanesi ◽  
Christian Massari ◽  
Alexander Gruber ◽  
Luca Brocca ◽  
Hans Lievens ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
River Valley ◽  
System Level ◽  
Surface Model ◽  
Irrigation Scheme ◽  
Po River ◽  
Area Index ◽  
The Impact

<p>Worldwide, the amount of water used for agricultural purposes is rising because of an increasing food demand. In this context, the detection and quantification of irrigation is crucial, but the availability of ground observations is limited. Therefore, an increasing number of studies are focusing on the use of models and satellite data to detect and quantify irrigation. For instance, the parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still characterized by simplifying assumptions, such as the lack of dynamic crop information, the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining models and satellite information through data assimilation can offer a viable way to quantify the water used for irrigation.</p><p>The aim of this study is to test how well modelled soil moisture and vegetation estimates from the Noah-MP LSM, with or without irrigation parameterization in the NASA Land Information System (LIS), are able to mimic in situ observations or to capture the signal of high-resolution Sentinel-1 backscatter observations in an irrigated area. The experiments were carried out over select sites in the Po river Valley, an important agricultural area in Northern Italy. To prepare for a data assimilation system, Level-1 Sentinel-1 backscatter observations, aggregated and sampled onto the 1 km EASE-v2 grid, were used to calibrate a Water Cloud Model (WCM) using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP. Results demonstrate that the use of the irrigation scheme provides the optimal calibration of the WCM, confirming the ability of Sentinel-1 to track the impact of human activities on the water cycle. Additionally, a first data assimilation experiment demonstrates the potential of Sentinel-1 backscatter observations to correct errors in Land Surface Model (LSM) simulations that are caused by unmodelled or wrongly modelled irrigation.</p>

Irrigation detection with Sentinel-1 radar backscatter observations over an agricultural area in the Po River Valley (Italy)

2020 ◽  
Author(s):  
Sara Modanesi ◽  
Gabriëlle J. M. De Lannoy ◽  
Alexander Gruber ◽  
Christian Massari ◽  
Luca Brocca ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
River Valley ◽  
System Level ◽  
Surface Model ◽  
Po River ◽  
In Situ Data ◽  
Level 1

<p>Given the projected decrease in water availability due to climate change and anthropogenic processes, the quantification of water usage for agricultural purposes is of critical importance. However, an accurate quantification of irrigation and groundwater extraction remains a major challenge for the current generation of scientists. For instance, the parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but still suffers from simplified assumptions, such as the mostly unknown timing and quantity of irrigation, often for lack of enough ground-based data. Remote sensing observations offer an opportunity to fill this gap in our knowledge, as they will detect irrigation activities. Earlier studies have used satellite soil moisture products obtained from microwave sensors to detect irrigated areas, but only some studies have dealt with the quantification of irrigation using satellite soil moisture data.</p><p>The aim of this study is to investigate the ability of high-resolution Sentinel-1 observations to detect changes in soil moisture and vegetation caused by irrigation fluxes. The focus area is the Po river Valley, one of the most important agricultural areas in Northern Italy, where in situ data are available for evaluation at four pilot sites. A comparison of Level-2 Sentinel-1 soil moisture retrievals, in situ data and Noah-MP land surface model (LSM) estimates confirms the presence of irrigation at the pilot sites. However, we hypothesize that even more information on both the irrigated soil moisture and vegetation can be extracted from the Level-1 Sentinel-1 signal via backscatter data assimilation. To prepare for such an assimilation system, Level-1 Sentinel-1 backscatter observations, pre-processed to the 1 km EASE-v2 grid, are further compared to the total backscatter simulated by a Water Cloud Model, using the simulated soil moisture obtained by the Noah-MP LSM as part of the NASA Land Information System (LIS). Noah-MP is here selected for its ability to simulate dynamic vegetation. Our results will show that irrigation can indeed also be detected from the mismatch between simulated and observed backscatter values.</p>

scholarly journals Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North

Soil Systems ◽  
2021 ◽  
Vol 5 (4) ◽  
pp. 57
Author(s):  
Umesh Acharya ◽  
Aaron L. M. Daigh ◽  
Peter G. Oduor
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Regression Trees ◽  
River Valley ◽  
Red River ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Red River Valley ◽  
The North ◽  
Learning Techniques

Precise soil moisture prediction is important for water management and logistics of on-farm operations. However, soil moisture is affected by various soil, crop, and meteorological factors, and it is difficult to establish ideal mathematical models for moisture prediction. We investigated various machine learning techniques for predicting soil moisture in the Red River Valley of the North (RRVN). Specifically, the evaluated machine learning techniques included classification and regression trees (CART), random forest regression (RFR), boosted regression trees (BRT), multiple linear regression (MLR), support vector regression (SVR), and artificial neural networks (ANN). The objective of this study was to determine the effectiveness of these machine learning techniques and evaluate the importance of predictor variables. The RFR and BRT algorithms performed the best, with mean absolute errors (MAE) of <0.040 m3 m−3 and root mean square errors (RMSE) of 0.045 and 0.048 m3 m−3, respectively. Similarly, RFR, SVR, and BRT showed high correlations (r2 of 0.72, 0.65 and 0.67 respectively) between predicted and measured soil moisture. The CART, RFR, and BRT models showed that soil moisture at nearby weather stations had the highest relative influence on moisture prediction, followed by 4-day cumulative rainfall and PET, subsequently followed by bulk density and Ksat.

scholarly journals The impact of beaver ponds on tree stand in a river valley

2009 ◽  
Vol 13a (1) ◽  
pp. 313-327 ◽  
Author(s):  
Andrzej Boczoń ◽  
Michał Wróbel ◽  
Valentyn Syniaiev
Keyword(s):  
Soil Moisture ◽  
Water Retention ◽  
Riparian Forest ◽  
Field Studies ◽  
River Valley ◽  
Tree Stand ◽  
Beaver Ponds ◽  
River Valleys ◽  
The Impact

The impact of beaver ponds on tree stand in a river valley The number of beavers in Poland rapidly increases which may result in conflicts between man and beavers. Despite the fact that beaver ponds play important role in increasing of biodiversity, water retention and soil moisture, they may also cause the die out of tree stands in river valleys and lead consequently to disappearance of typical riparian forest communities. Field studies demonstrated that long term flooding inhibited tree growth. Many trees died after 2 years of flooding. Long flooding caused the death of 80% of trees.

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